Abstract
Adapter-based methods are commonly used to enhance model performance with minimal additional complexity, especially in video editing tasks that require frame-to-frame consistency. By inserting small, learnable modules into pretrained diffusion models, these adapters can maintain temporal coherence without extensive retraining. Approaches that incorporate prompt learning with both shared and frame-specific tokens are particularly effective in preserving continuity across frames at low training cost. In this work, we want to provide a general theoretical framework for adapters that maintain frame consistency in DDIM-based models under a temporal consistency loss. First, we prove that the temporal consistency objective is differentiable under bounded feature norms, and we establish a Lipschitz bound on its gradient. Second, we show that gradient descent on this objective decreases the loss monotonically and converges to a local minimum if the learning rate is within an appropriate range. Finally, we analyze the stability of modules in the DDIM inversion procedure, showing that the associated error remains controlled. These theoretical findings will reinforce the reliability of diffusion-based video editing methods that rely on adapter strategies and provide theoretical insights in video generation tasks.
Abstract (translated)
基于适配器的方法通常用于在最小增加复杂度的情况下增强模型性能，特别是在需要帧到帧一致性的视频编辑任务中。通过将小型可学习模块插入预训练的扩散模型中，这些适配器可以在无需大量重新训练的情况下保持时间一致性。结合提示学习（使用共享和特定于每一帧的标记）的方法特别有效，在低训练成本下跨帧维持连续性。 在这项工作中，我们希望为在DDIM（Denoising Diffusion Implicit Models）基于模型中保持帧一致性的适配器提供一个通用理论框架，并在此基础上引入时间一致性损失。首先，我们将证明当特征范数有界时，时间一致性目标是可微的，并且我们建立了其梯度上的Lipschitz边界。其次，我们展示在适当的学习率范围内进行梯度下降会使该目标的损失单调减少并最终收敛到局部最小值。最后，我们分析了DDIM反演过程中的模块稳定性，证明相关误差保持受控。 这些理论发现将增强基于扩散方法依赖适配器策略进行视频编辑技术的可靠性，并为视频生成任务提供理论见解。
URL
https://arxiv.org/abs/2504.16016
